Artificial neural networks can potentially control autonomous robots, vehicles, factories, or gameplayers more robustly than traditional approaches. Neuroevolution, i.e. the artificial evolution ofneural networks, is a method for finding the right topology and connection weights to specify thedesired control behavior. The challenge for neuroevolution is that difficult tasks may require complexnetworks with many connections, all of which must be set to the right values. Even if a networkexists that can solve the task, evolution may not be able to find it in such a high-dimensional searchspace. This dissertation presents the NeuroEvolution of Augmenting Topologies (NEAT) method,which makes search for complex solutions feasible. In a process called complexification, NEATbegins by searching in a space of simple networks, and gradually makes them more complex as thesearch progresses. By starting minimally, NEAT is more likely to find efficient and robust solutionsthan neuroevolution methods that begin with large fixed or randomized topologies; by elaboratingon existing solutions, it can gradually construct even highly complex solutions. In this dissertation,NEAT is first shown faster than traditional approaches on a challenging reinforcement learningbenchmark task. Second, by building on existing structure, it is shown to maintain an پarms race..,. tested for theQ1 PC1 bus cardBoth these projects mere sofixare des elopment efforts tonards contributing to dlfferentaspects of Roboucs and lZ1echatronics projects m the Controls and Roboucs Group..